• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Journal of Korean Society of Rural Planning
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• v.28 no.1
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Smart Media Journal
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• v.12 no.10
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• pp.47-54
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• 2023

As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.134.2-135
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• 2003

The plant-pathogenic species S. scabies, S. acidiscabies, and S. turgidiscabies cause the scab disease of potato and produce the phytotoxins, thaxtomins. necl, a gene conferring a necrogenic phenotype, is involved in pathogenicity and physically linked to the thaxtomin A biosynthetic genes. Identification of the pathogenic strains of Streptomyces from soil was performed through the polymerase chain reaction by using specific pathogenicity primer sets derived from the necl gene sequences of Streptomyces smbies. The DNA was extracted from soil using a bead-beating machine and modifications of the FastPrep system. The DNA was suitable for direct use in the PCR. The PCR products showed the bands of approximately 460 bp. This methods can be very usuful in identifying species responsible for scab diseases and studying on the ecology of plant-pathogenic Streptomyces spp.

• Journal of Biosystems Engineering
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• v.31 no.1 s.114
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• pp.59-64
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• 2006

Application of herbicide for rice cropping is inevitable but notorious for its side effect of environmental pollution. Precision fanning will be one of important tools for the least input and sustainable fanning and could be achieved by implementation of the variable rating technology. If a device to detect weeds in rice field is available, herbicide could be applied only to the places where it is needed by the manner of the variable rating technology. The study was carried out to develop an algorithm of image processing to detect weeds in rice field using a machine vision system of multi-spectral digital images. A series of multi-spectral rice field picture of 560, 680 and 800 nm of center wavelengths were acquired from the 27th day to the 39th day after transplanting in the ineffective tillering stage of a rice growing period. A discrimination model to distinguish pixels of weeds from those of rice plant and weed image was developed. The model was proved as having accuracies of 83.6% and 58.9% for identifying the rice plant and the weed, respectively. The model was used in the algorithm to differentiate weed images from mingled images of rice plant and weed in a frame of rice field picture. The developed algorithm was tested with the acquired rice field pictures and resulted that 82.7%, 11.9% and 5.4% of weeds in the pictures were noted as the correctly detected, the undetected and the misclassified as rice, respectively, and 81.9% and 18.0% of rice plants in the pictures were marked as the correctly detected and the misclassified as weed, respectively.

• Journal of Biosystems Engineering
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• v.7 no.2
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• pp.36-44
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• 1983

Farm population was rapidly decreasing due to shift of the people from farm sector to the non-farm sector caused by the economic growth of the country. Especially, a great shortage of farm labor in busy farming period in June and October is becoming a serious problem in maintaining or promoting land productivity. The peak of labor requirement in summer is caused by rice transplanting and barley harvesting. In order to reduce the restrictions imposed on farm management by the concurrence of labor requirement and the lack of labor, the experimental study for mechanization of barley harvesting has been carried out in the fields. 1. The machines for barley harvesting were knap-sack type reapers, windrow reaper (power tiller attachment), binder and combine. The order of higher efficiency of machine for barley harvesting was combine, binder, windrow reaper (WR), knapsack type reaper 1(KSTR1), and knap sack type reaper 2(KSTR2; mist and duster attachment). 2. The ratio of grain loss for the manual, binder, and combine plot was about four percent of total field yield. 3. The total yield of barley in 35 days and 40 days harvesting after heading were 514 kg and 507kg per 10 ares respectively. The yield of 35 days-plot was higher than other experimental plots. 4. The lowest yield was recorded in 30 days-plot due to the large quantity of immatured grains and having lighter 1000-grain weight. The ratio of immatured grains was 2.66 percent and 1000-grain weight was 29.4 grams. 5. The total harvesting cost of the windrow reaper was 10,178 won per 10 ares. It was the lowest value compared to other machines. The next were combine, binder, KSTR1, KSTR2, and manual in sequence. As a result, the optimum time of barley harvesting for mechanization was 35-40 days after heading. Combine, binder, and windrow reaper were recommended as the suitable machines for barley harvesting in the work efficiency. However, in total harvesting cost, the windrow reaper was the most promising machine for barley harvesting.

• Journal of Biosystems Engineering
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• v.42 no.2
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• pp.75-79
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• 2017

The use of tractors to carry out agricultural work has played an important role in mechanizing the agricultural sector. A repairable mechanical system (such as an agricultural tractor) is subject to deterioration or failure. In this study, a regression model was used to predict the failure rate of a John Deere 3140 tractor. The machine failure pattern was carefully studied, and key factors affecting the failure rate were identified in five regions of the Khouzestan province. Through a questionnaire, data was obtained from farm records. This data was grouped into six sub-groups, according to the annual use hours (AUH) and the manner in which the tractors were stored. Results showed that AUH and storage policies affected failure rate slightly. With an increase in the age of the tractors, the failure rate in the tractors used for 1050-2000 hours annually and stored outdoors was higher than those used for 200-1000 hours annually and stored in sheds. When the tractors were of the same age, the slope of the curve in the 200-1000 annual use hours increased gradually and then rapidly, but failure rate in the 1050-2000 annual use hours was high from the beginning, and subsequent increase in this value was almost uniform. As a result, it can be said that with an increase in the annual use hours, the failure and breakdown rate in John Deere 3140 tractors rapidly increases, but maintenance conditions only slightly affect the failure and breakdown rate.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.315-326
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• 2020

In this study, a real-time crop recognition system was developed for an unmanned farm machine for upland farming. The crop recognition system was developed based on a stereo camera, and an image processing framework was proposed that consists of disparity matching, localization of crop area, and estimation of crop height with coordinate transformations. The performance was evaluated by attaching the crop recognition system to a tractor for five representative crops (cabbage, potato, sesame, radish, and soybean). The test condition was set at 3 levels of distances to the crop (100, 150, and 200 cm) and 5 levels of camera height (42, 44, 46, 48, and 50 cm). The mean relative error (MRE) was used to compare the height between the measured and estimated results. As a result, the MRE of Chinese cabbage was the lowest at 1.70%, and the MRE of soybean was the highest at 4.97%. It is considered that the MRE of the crop which has more similar distribution lower. the results showed that all crop height was estimated with less than 5% MRE. The developed crop recognition system can be applied to various agricultural machinery which enhances the accuracy of crop detection and its performance in various illumination conditions.

• Food Engineering Progress
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• v.21 no.1
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• pp.57-63
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• 2017

This study investigated the effects of 27.12 MHz radio frequency (RF) heating on heat transfer phenomena during the thawing process of frozen food. To determine the velocity of the RF thawing machine, samples were frozen at $-80^{\circ}C$ and subjected to different power treatments. The phase change times (-5 to $0^{\circ}C$) of frozen radish were 30, 26, 13, and 8 min; those of pork sirloin were 38, 25, 11, and 5 min; those of rump were 23, 17, 11, and 6 min; those of chicken breast were 42, 29, 13, and 9 min; and those of tuna were 25, 23, 10, and 5 min at 50, 100, 200, and 400 W, respectively. The heating limit temperatures of the radish, pork sirloin, rump, chicken breast, and tuna samples were 19.5, 9.2, 21.8, 8.8, and $16.8^{\circ}C$ at 50 W; 23.5, 15.5, 27.3, 12.3, and $19^{\circ}C$ at 100 W; 42, 26.9, 45.7, 22.1, and $39.4^{\circ}C$ at 200 W; and 48.5, 54.7, 63.6, 57.3, and $44.9^{\circ}C$ at 400 W. These results suggest that high-power RF improves thawing velocity and heating limit temperatures, and that an improvement on the operation of the RF thawing machine, according to food temperatures, is needed.